Effect of the Albumin Corona on the Toxicity of Combined Graphene Oxide and Cadmium to Daphnia magna and Integration of the Datasets into the NanoCommons Knowledge Base

In this work, we evaluated the effect of protein corona formation on graphene oxide (GO) mixture toxicity testing (i.e., co-exposure) using the model and assessing acute toxicity determined as immobilisation. Cadmium (Cd ) and bovine serum albumin (BSA) were selected as co-pollutant and protein mode...

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Veröffentlicht in:Nanomaterials (Basel, Switzerland) Switzerland), 2020-09, Vol.10 (10), p.1936
Hauptverfasser: Martinez, Diego Stéfani T, Da Silva, Gabriela H, de Medeiros, Aline Maria Z, Khan, Latif U, Papadiamantis, Anastasios G, Lynch, Iseult
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Sprache:eng
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Zusammenfassung:In this work, we evaluated the effect of protein corona formation on graphene oxide (GO) mixture toxicity testing (i.e., co-exposure) using the model and assessing acute toxicity determined as immobilisation. Cadmium (Cd ) and bovine serum albumin (BSA) were selected as co-pollutant and protein model system, respectively. Albumin corona formation on GO dramatically increased its colloidal stability (ca. 60%) and Cd adsorption capacity (ca. 4.5 times) in reconstituted water ( medium). The acute toxicity values (48 h-EC ) observed were 0.18 mg L for Cd -only and 0.29 and 0.61 mg L following co-exposure of Cd with GO and BSA@GO materials, respectively, at a fixed non-toxic concentration of 1.0 mg L . After coronation of GO with BSA, a reduction in cadmium toxicity of 110 % and 238% was achieved when compared to bare GO and Cd -only, respectively. Integration of datasets associated with graphene-based materials, heavy metals and mixture toxicity is essential to enable re-use of the data and facilitate nanoinformatics approaches for design of safer nanomaterials for water quality monitoring and remediation technologies. Hence, all data from this work were annotated and integrated into the NanoCommons Knowledge Base, connecting the experimental data to nanoinformatics platforms under the FAIR data principles and making them interoperable with similar datasets.
ISSN:2079-4991
2079-4991
DOI:10.3390/nano10101936